Method and equipment for constant temperature control of printer nozzle and storage medium

Abstract

The invention discloses a method and equipment for constant temperature control of a printer nozzle and a storage medium, and relates to the technical field of printer nozzle temperature control, temperature data is collected in real time through a distributed temperature sensor array, and PID parameters are adjusted by combining environment temperature and humidity and ink flow and utilizing a dynamic self-adaptive control algorithm model, so that the temperature of the printer nozzle is controlled. The method comprises feedforward control, partition gradient heating, a pulse type low-power-consumption heating mode and an abnormal temperature processing mechanism, precise control over the temperature of the spray head is achieved, the electronic equipment comprises a multi-sensor fusion module, a data processing module, a power driving module and a state monitoring module, efficient execution of a control strategy is ensured, and the control efficiency is improved. According to the method and the equipment, the control strategy is continuously optimized through the self-adaptive learning function, and the printing quality and the equipment stability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of printer nozzle temperature control, and more specifically, to a method, device, and storage medium for constant temperature control of a printer nozzle. Background Art

[0002] In modern printers, the constant temperature control of the nozzle is an important link to ensure printing quality. Traditional nozzle constant temperature control methods mainly rely on a single temperature sensor and a PID control algorithm with a fixed threshold. This method maintains the constant temperature state of the nozzle by detecting the overall temperature of the nozzle and feedback-adjusting the power of the heating element. However, with the complexity of printing tasks and the change of environmental conditions, traditional methods gradually expose problems such as response lag, uneven local temperature, poor environmental adaptability, and high energy consumption; specifically, when the nozzle starts and stops frequently or the printing speed changes suddenly, the temperature adjustment delay of the traditional PID algorithm is obvious, which is easy to cause overshoot or undershoot; a single sensor cannot reflect the overall temperature distribution of the nozzle, which is easy to cause local overheating or low-temperature blockage, and even damage the nozzle; the variables such as environmental temperature and humidity and ink viscosity are not dynamically combined, resulting in low constant temperature accuracy; in addition, the fixed threshold control causes the heating element to turn on and off frequently, which not only shortens the service life of the heating element, but also increases the energy consumption.

[0003] Therefore, the prior art has problems of response lag, uneven local temperature, poor environmental adaptability, and high energy consumption. Summary of the Invention

[0004] In order to overcome the problems of response lag, uneven local temperature, poor environmental adaptability, and high energy consumption in the prior art, the present invention discloses a method, device, and storage medium for constant temperature control of a printer nozzle, which can effectively solve the above technical problems.

[0005] To solve the above technical problems, the technical solution of the present invention is as follows:

[0006] A method for constant temperature control of a printer nozzle includes the following steps:

[0007] Real-time collect the temperature data of each partition on the surface of the nozzle through a distributed temperature sensor array, and at the same time obtain the environmental temperature and humidity parameters and ink flow data;

[0008] Input the temperature data, environmental temperature and humidity parameters, and ink flow data into a dynamic adaptive control algorithm model, and dynamically adjust the proportional, integral, and differential coefficients of the PID controller through fuzzy logic;

[0009] Predict the temperature fluctuation trend of the nozzle based on the printing task instruction, and generate a pre-compensation instruction through the feedforward control module;

[0010] Divide the independent temperature control area according to the temperature difference of the partition, dynamically allocate the heating power of each area, and preferentially compensate the heating demand of the low-temperature area;

[0011] When it is detected that the print head is in the idle state, switch to the pulse-type low-power heating mode to periodically maintain the base temperature;

[0012] Closed-loop feedback regulates the PWM duty cycle of the heating element, and combines with the active heat dissipation device to achieve stable temperature control.

[0013] Preferably, the specific content of the dynamic adaptive control algorithm model includes:

[0014] Establish a fuzzy logic rule base according to the temperature deviation change rate, and map the temperature deviation and deviation change rate to the fuzzy domain through the membership function;

[0015] Based on the correlation between the real-time environmental parameters and the ink viscosity, correct the adjustment range of the PID parameters; when the temperature deviation exceeds the preset threshold, trigger the non-linear PID control mode.

[0016] Preferably, the generation logic of the feed-forward control module includes:

[0017] Analyze the inkjet frequency and printing speed parameters in the printing task, and establish a prediction model in combination with the historical temperature change data;

[0018] Calculate the expected temperature fluctuation amplitude in the future time period through the sliding time window algorithm;

[0019] Generate a power compensation instruction inversely proportional to the expected fluctuation amplitude, and adjust the working state of the heating element in advance.

[0020] Preferably, the implementation method of the partition gradient heating is:

[0021] Divide the surface of the print head into an N×M matrix temperature control grid, and each grid corresponds to an independent heating unit;

[0022] Calculate the difference between the temperature of each grid and the target temperature, and generate a priority weight matrix;

[0023] Dynamically allocate the PWM duty cycle according to the weight matrix, and the areas with weight values higher than the preset threshold preferentially obtain full-power heating.

[0024] Preferably, the pulse-type low-power heating mode includes:

[0025] When it is detected that the idle time of the print head exceeds the first preset time threshold, start the intermittent heating program;

[0026] Set the pulse period to T, turn on the heating for the first t1 time and turn off the heating for the remaining (T - t1) time within each cycle, where t1 < T;

[0027] When the ambient temperature is lower than the second preset threshold, the pulse period T is automatically shortened and the t1 time is prolonged.

[0028] Preferably, the method further includes an abnormal temperature processing mechanism:

[0029] When the temperature of any partition exceeds the safety threshold, the heating power supply of this area is immediately cut off and a semiconductor refrigeration chip is started;

[0030] The heating power of adjacent areas is synchronously reduced until the temperature drops back to the target range;

[0031] Record the environmental parameters and printing task characteristics when the abnormal event occurs, which is used to optimize the subsequent control strategy.

[0032] Preferably, the method further includes an adaptive learning function:

[0033] Collect the temperature deviation data, environmental parameter changes and control response effects in the historical control process;

[0034] Generate an optimal PID parameter adjustment strategy through the reinforcement learning algorithm;

[0035] Update the training result to the fuzzy logic rule base to realize the self-iterative optimization of the control model.

[0036] An electronic device, characterized in that it includes:

[0037] A multi-sensor fusion module, including a distributed temperature sensor array, an environmental temperature and humidity sensor, and an ink flow monitoring unit;

[0038] A data processing module, used to execute the control algorithm of the method as described above;

[0039] A power driving module, including multiple independent PWM output ports, which are respectively connected to each partition heating unit and a heat dissipation device;

[0040] A status monitoring module, which feeds back the working mode of the nozzle and the temperature distribution data to the central controller in real time.

[0041] Preferably, the hardware implementation of the power driving module includes:

[0042] Use a multi-channel digital isolator to achieve electrical isolation between heating units;

[0043] Each heating unit is equipped with an independent overcurrent protection circuit and a temperature fuse;

[0044] The heat dissipation device includes a micro fan array and a semiconductor refrigeration chip, and the refrigeration direction is controlled by an H-bridge circuit.

[0045] A computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, the steps of the above-described method are implemented.

[0046] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention uses a multi-sensor fusion module to collect in real time the temperature data of each partition on the surface of the print head, the ambient temperature and humidity parameters, and the ink flow data, and inputs these data into a dynamic adaptive control algorithm model. The fuzzy logic dynamically adjusts the proportional, integral, and differential coefficients of the PID controller to achieve precise temperature control. At the same time, the feed-forward control module predicts the temperature fluctuation trend of the print head based on the print task instruction, and generates a pre-compensation instruction in advance to effectively reduce the temperature fluctuation. Therefore, the overall temperature fluctuation range of the print head is reduced from ±3°C to ±0.5°C, reducing the risk of nozzle clogging; the implementation method of zonal gradient heating ensures uniform temperature in each area of the print head. The surface of the print head is divided into an N×M matrix temperature control grid, and each grid corresponds to an independent heating unit. The difference between the temperature of each grid and the target temperature is calculated to generate a priority weight matrix, and the PWM duty cycle is dynamically allocated according to the weight matrix to preferentially compensate the heating requirements of the low-temperature area. The uniform temperature distribution ensures the consistency of ink viscosity, improves color accuracy and edge sharpness; when the print head is in the idle state, it switches to the pulse-type low-power heating mode to periodically maintain the base temperature. This mode reduces energy consumption through an intermittent heating program while ensuring that the temperature of the print head remains at the base level. In addition, the heating power of each area is dynamically allocated to preferentially compensate the heating requirements of the low-temperature area and reduce ineffective heating; the dynamic adaptive control algorithm model corrects the adjustment range of the PID parameters based on the correlation between the real-time environmental parameters and the ink viscosity to adapt to different environmental conditions. The abnormal temperature processing mechanism ensures timely adjustment in case of abnormal temperature, records the environmental parameters and print task characteristics when the abnormal event occurs, and is used to optimize the subsequent control strategy, and realizes self-learning compensation by integrating environmental parameters to adapt to different climates and ink types; when the temperature of any partition exceeds the safety threshold, the abnormal temperature processing mechanism immediately cuts off the heating power of that area and starts the semiconductor refrigeration sheet, and synchronously reduces the heating power of the adjacent areas until the temperature drops back to the target range, effectively preventing the print head from being damaged due to overheating and improving the reliability and safety of the system; by collecting the temperature deviation data, environmental parameter changes, and control response effects in the historical control process, using the reinforcement learning algorithm to train and generate the optimal PID parameter adjustment strategy, and updating the training results to the fuzzy logic rule base, the self-iteration optimization of the control model is realized, which can continuously improve the control accuracy and response speed, and ensure that the system maintains high performance during long-term use. Description of the Drawings

[0047] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only exemplary. For those of ordinary skill in the art, without creative efforts, other implementation drawings can be obtained by extending the provided drawings.

[0048] Figure 1 It is the hierarchical structure diagram of the printer nozzle constant temperature control system;

[0049] Figure 2 It is the flow chart of the dynamic adaptive PID control algorithm;

[0050] Figure 3 It is the logic diagram of the nozzle temperature control state switching;

[0051] Figure 4 It is the interaction timing diagram of the components of the nozzle constant temperature control system;

[0052] Figure 5 It is the method step diagram of the present invention. Specific embodiments

[0053] The drawings are only for exemplary illustration and cannot be construed as a limitation of this patent;

[0054] To better illustrate this embodiment, some components in the drawings will be omitted, enlarged or reduced, which do not represent the dimensions of the actual product;

[0055] For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0056] The following will further illustrate the technical solutions of the present invention in conjunction with the drawings and embodiments.

[0057] Embodiment

[0058] A method for controlling the constant temperature of a printer nozzle includes the following steps:

[0059] Real-time collect the temperature data of each partition on the nozzle surface through a distributed temperature sensor array, and at the same time obtain the environmental temperature and humidity parameters and ink flow data;

[0060] Input the temperature data, environmental temperature and humidity parameters, and ink flow data into the dynamic adaptive control algorithm model, and dynamically adjust the proportional, integral, and differential coefficients of the PID controller through fuzzy logic;

[0061] Predict the nozzle temperature fluctuation trend based on the printing task instruction, and generate a pre-compensation instruction through the feedforward control module;

[0062] Divide independent temperature control regions according to the temperature difference between partitions, dynamically allocate the heating power of each region, and preferentially compensate the heating requirements of low-temperature regions;

[0063] When it is detected that the print head is in an idle state, switch to the pulse-type low-power heating mode to periodically maintain the base temperature;

[0064] Close-loop feedback regulates the PWM duty cycle of the heating element and combines with the active heat dissipation device to achieve stable temperature control.

[0065] The specific content of the dynamic adaptive control algorithm model includes:

[0066] Establish a fuzzy logic rule base according to the temperature deviation change rate, and map the temperature deviation and deviation change rate to the fuzzy domain through the membership function;

[0067] Based on the correlation between real-time environmental parameters and ink viscosity, correct the adjustment amplitude of PID parameters; when the temperature deviation exceeds the preset threshold, trigger the non-linear PID control mode.

[0068] The generation logic of the feed-forward control module includes:

[0069] Analyze the inkjet frequency and printing speed parameters in the printing task, and establish a prediction model in combination with historical temperature change data;

[0070] Calculate the expected temperature fluctuation amplitude in the future time period through the sliding time window algorithm;

[0071] Generate a power compensation instruction inversely proportional to the expected fluctuation amplitude and adjust the working state of the heating element in advance.

[0072] The implementation method of the partition gradient heating is as follows:

[0073] Divide the surface of the print head into an N×M matrix temperature control grid, and each grid corresponds to an independent heating unit;

[0074] Calculate the difference between the temperature of each grid and the target temperature, and generate a priority weight matrix;

[0075] Dynamically allocate the PWM duty cycle according to the weight matrix, and the regions with weight values higher than the preset threshold preferentially obtain full-power heating.

[0076] The pulse-type low-power heating mode includes:

[0077] When it is detected that the idle time of the print head exceeds the first preset time threshold, start the intermittent heating program;

[0078] Set the pulse period to T, turn on the heating in the first t1 time and turn off the heating in the subsequent (T - t1) time within each cycle, where t1 < T;

[0079] When the ambient temperature is lower than the second preset threshold, automatically shorten the pulse period T and extend the t1 time.

[0080] The method further includes an abnormal temperature handling mechanism:

[0081] When the temperature of any partition exceeds the safety threshold, immediately cut off the heating power of that area and start the thermoelectric cooler;

[0082] Synchronously reduce the heating power of adjacent areas until the temperature drops back to the target range;

[0083] Record the environmental parameters and printing task characteristics when the abnormal event occurs, for optimizing subsequent control strategies.

[0084] The method further includes an adaptive learning function:

[0085] Collect the temperature deviation data, environmental parameter changes, and control response effects in the historical control process;

[0086] Generate an optimal PID parameter adjustment strategy through reinforcement learning algorithms;

[0087] Update the training results to the fuzzy logic rule base to achieve self-iterative optimization of the control model.

[0088] An electronic device, comprising:

[0089] A multi-sensor fusion module, including a distributed temperature sensor array, an environmental temperature and humidity sensor, and an ink flow monitoring unit;

[0090] A data processing module for executing the control algorithm of the method as described above;

[0091] A power driving module, including multiple independent PWM output ports, respectively connected to each partition heating unit and the heat dissipation device;

[0092] A status monitoring module for real-time feedback of the nozzle working mode and temperature distribution data to the central controller.

[0093] The hardware implementation of the power driving module includes:

[0094] Use a multi-channel digital isolator to achieve electrical isolation between heating units;

[0095] Each heating unit is equipped with an independent overcurrent protection circuit and a thermal fuse;

[0096] The heat dissipation device includes a micro fan array and a thermoelectric cooler, and controls the refrigeration direction through an H-bridge circuit.

[0097] A computer-readable storage medium has a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned method are implemented.

[0098] In a specific implementation, refer to Figures 1-5 , the hardware system:

[0099] The multi-sensor fusion module is on the surface of the printer nozzle. According to the nozzle structure and heat distribution characteristics, a distributed temperature sensor array ④ is evenly installed. The number and position of the sensors are designed to ensure that all key areas of the nozzle can be comprehensively covered, such as near the inkjet orifice, near the heating element, etc., so as to obtain the temperature data of each partition in real time. An ambient temperature and humidity sensor ⑤ is installed near the air inlet of the nozzle housing to monitor the ambient temperature and humidity parameters in real time; an ink flow monitoring module ⑥ is integrated on the ink delivery pipe to accurately collect the ink flow data. Each sensor is connected to the data processing module through a dedicated shielded cable to reduce electromagnetic interference and ensure the accuracy and stability of data transmission.

[0100] The data processing module selects a high-performance microcontroller as the data processing core, such as the STM32 series. In the development environment, initialization code is written to configure each peripheral interface of the microcontroller so that it can communicate smoothly with the multi-sensor fusion module, the power drive module, and the status monitoring module. The program code implementing dynamic adaptive control algorithms, feedforward control algorithms, etc. is burned into the internal storage of the microcontroller. These algorithm programs will process the data collected by the sensors in real time to generate accurate control instructions to achieve the control of the nozzle temperature.

[0101] The power drive module uses a multi-channel digital isolator, such as the ADuM series, to achieve electrical isolation between the heating units of each partition, ensuring that each heating unit can work independently and stably without interference. An independent overcurrent protection circuit is equipped for each heating unit. This circuit consists of a sampling resistor and a comparator. When the sampling resistor detects that the current exceeds the preset threshold, the comparator outputs a signal to trigger the microcontroller to cut off the power supply of the corresponding heating unit to prevent overcurrent damage to the heating element. At the same time, a temperature fuse is installed on each heating unit. When the temperature reaches the fusing temperature, the fuse cuts off the circuit to ensure the safety of the device. The heat dissipation device consists of a micro fan array and a thermoelectric cooler. The micro fans are evenly distributed around the nozzle to take away heat through forced air cooling; the thermoelectric cooler is installed in a specific heat dissipation area of the nozzle. The control of the refrigeration direction is built through an H-bridge circuit, such as an L298N chip. The multiple independent PWM output ports of the power drive module are respectively connected to the drive circuits ⑧ of each partition heating unit ⑨ and the heat dissipation device, and the heating and heat dissipation powers are accurately controlled according to the instructions of the microcontroller.

[0102] The status monitoring module installs microswitches on the moving parts of the printhead, such as the guide rail of the printhead carriage, and additional temperature sensors at key positions on the ink chamber and the printhead surface. These sensors work together to monitor the working mode of the printhead in real time, such as printing, idle, cleaning, etc., and the temperature distribution of each partition. The status monitoring module feeds the collected data back to the central controller, i.e., the microcontroller, in real time through communication interfaces such as SPI or I2C, providing a basis for the system to adjust the control strategy.

[0103] Software algorithm implementation:

[0104] Multi-dimensional data acquisition and preprocessing After the printer is started, the multi-sensor fusion module starts to work. The distributed temperature sensor array ④, the ambient temperature and humidity sensor ⑤, and the ink flow monitoring module ⑥ collect data at the set acquisition frequencies. For example, the temperature sensor collects data every 50 ms, and the temperature and humidity and flow sensors collect data every 100 ms. The collected data first enters the buffer of the microcontroller and then is preprocessed through software filtering algorithms. For example, the Kalman filtering algorithm is used to remove the noise in the temperature data to improve the data accuracy.

[0105] According to the temperature deviation change rate, a fuzzy logic rule base is constructed in the microcontroller program. The temperature deviation is divided into multiple fuzzy subsets, such as negative large, negative medium, negative small, zero, positive small, positive medium, positive large; the deviation change rate is also divided similarly. The triangular membership function is used to map the temperature deviation and the deviation change rate to the fuzzy domain. For example, when the temperature deviation is positive large and the deviation change rate is positive small, the fuzzy logic rule base indicates to appropriately reduce the proportional coefficient, increase the integral coefficient, and fine-tune the differential coefficient. The microcontroller calculates the temperature deviation and the deviation change rate in real time and queries the rule base to obtain the adjustment direction and amplitude of the PID parameters.

[0106] An association table of environmental parameters (temperature and humidity) and ink viscosity is pre-stored in the microcontroller memory. The microcontroller queries the table according to the real-time collected environmental temperature and humidity to obtain the viscosity correction coefficient, and then corrects the adjustment amplitude of the PID parameters. When the temperature deviation exceeds the preset threshold, such as ±1.5 °C, the non-linear PID control mode is triggered. In this mode, a segmented PID control strategy is adopted according to the size of the temperature deviation to improve the control accuracy.

[0107] After receiving the printing task instruction, the microcontroller analyzes the inkjet frequency and printing speed parameters therein, and combines with the historical temperature change data stored internally to establish a prediction model using the linear regression algorithm. After each printing task is completed, the model coefficients are updated according to the actual temperature change to improve the prediction accuracy.

[0108] The sliding time window algorithm (window size set to 10 s) is used to calculate the expected temperature fluctuation amplitude in the next 5 s. If the prediction model shows that the temperature will rise, the microcontroller generates a power compensation instruction that is inversely proportional to the fluctuation amplitude. For example, when printing normally, the PWM duty cycle of the heating element is 50%, and when a temperature increase is expected, it is adjusted to 40% to suppress the temperature rise in advance.

[0109] The surface of the print head is divided into an N×M matrix temperature control grid. Each grid corresponds to an independent heating unit ⑨. Each heating unit consists of a heating resistor and a drive circuit, and they have the same electrical parameters and are electrically isolated from each other.

[0110] The microcontroller calculates the difference from the target temperature based on the temperature sensor data of each grid, generates a priority weight matrix. The larger the difference, the higher the weight. A preset weight threshold is set. The areas with weights higher than the threshold are given priority for full-power heating (PWM duty cycle set to 100%), and the areas below the threshold are allocated PWM duty cycles according to the weight ratio to preferentially compensate the low-temperature areas.

[0111] The working process of the pulse-type low-power heating mode: The microcontroller monitors the working state of the print head through the status monitoring module. When the idle time of the print head exceeds the first preset time threshold, an intermittent heating program is started. The pulse period is set to T. In each period, heating is turned on for the first t1 time and turned off for the remaining (T - t1) time. When the ambient temperature is lower than the second preset threshold, the pulse period T is automatically shortened and the t1 time is extended to maintain the base temperature and reduce energy consumption.

[0112] The microcontroller continuously monitors the temperature of each partition. When the temperature of any partition exceeds the safety threshold, the heating power supply of that area is immediately cut off through the power drive module, and a semiconductor refrigeration chip is started. At the same time, the heating power of the adjacent areas is reduced to prevent heat dissipation. After the temperature drops back to the target range, normal control is restored. The microcontroller records the environmental parameters and printing task characteristics of the abnormal event for optimizing the control strategy.

[0113] The microcontroller regularly collects the temperature deviation data, environmental parameter changes, and control response effects in the historical control process. After every certain number of printing tasks, the reinforcement learning algorithm, such as the Q-learning algorithm, is used to train and generate the optimal PID parameter adjustment strategy. The training results are updated to the fuzzy logic rule base to achieve the self-iterative optimization of the control model. During the operation of the entire system, the interaction layer ① communicates with the printer main control, synchronizes the printing task instructions, and feedbacks the status; the control layer ② generates partition heating instructions through the main control algorithm based on the collected data; the data acquisition layer ③ is responsible for collecting various data; the execution layer ⑦ executes the heating and cooling operations; when the temperature is high, the water cooling device ⑩ of the print head starts to assist in heat dissipation. Through the coordinated work of hardware and software, this system effectively improves the temperature stability of the print head, optimizes the printing quality, reduces energy consumption, and enhances environmental adaptability.

[0114] Like or similar reference numerals correspond to like or similar components;

[0115] The terms used to describe the positional relationship in the drawings are for illustrative purposes only and should not be construed as a limitation of this patent;

[0116] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention and are not limitations on the implementation manners of the present invention. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to enumerate all implementation manners here. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the claims of the present invention.

Claims

1. A method for constant temperature control of a printer nozzle, characterized in that, It includes the following steps: Collect the temperature data of each partition on the nozzle surface in real time through a distributed temperature sensor array, and at the same time obtain the ambient temperature and humidity parameters and ink flow data; Input the temperature data, ambient temperature and humidity parameters, and ink flow data into the dynamic adaptive control algorithm model, and dynamically adjust the proportional, integral, and differential coefficients of the PID controller through fuzzy logic; Predict the nozzle temperature fluctuation trend based on the printing task instruction, and generate a pre-compensation instruction through the feed-forward control module; Divide independent temperature control regions according to the partition temperature difference, dynamically allocate the heating power of each region, and preferentially compensate the heating requirements of the low-temperature regions; When it is detected that the nozzle is in the idle state, switch to the pulse-type low-power heating mode to periodically maintain the base temperature; Close-loop feedback regulates the PWM duty cycle of the heating element, and combines with the active heat dissipation device to achieve stable temperature control.

2. The method according to claim 1, wherein The specific content of the dynamic adaptive control algorithm model includes: Establish a fuzzy logic rule base according to the temperature deviation change rate, and map the temperature deviation and deviation change rate to the fuzzy domain through the membership function; Modify the adjustment range of the PID parameters based on the correlation between the real-time environmental parameters and the ink viscosity; when the temperature deviation exceeds the preset threshold, trigger the non-linear PID control mode.

3. The method according to claim 1, characterized in that The generation logic of the feed-forward control module includes: Analyze the inkjet frequency and printing speed parameters in the printing task, and establish a prediction model in combination with the historical temperature change data; Calculate the expected temperature fluctuation amplitude in the future time period through the sliding time window algorithm; Generate a power compensation instruction that is inversely proportional to the expected fluctuation amplitude, and adjust the working state of the heating element in advance.

4. The method according to claim 1, wherein The implementation method of the partition gradient heating is: Divide the nozzle surface into an N×M matrix temperature control grid, and each grid corresponds to an independent heating unit; Calculate the difference between the temperature of each grid and the target temperature, and generate a priority weight matrix; Dynamically allocate the PWM duty cycle according to the weight matrix, and the regions with weight values higher than the preset threshold preferentially obtain full-power heating.

5. The method according to claim 1, characterized in that The pulse-type low-power heating mode includes: When it is detected that the idle time of the nozzle exceeds the first preset time threshold, start the intermittent heating program; Set the pulse period as T, turn on the heating in the first t1 time within each cycle, and turn off the heating in the later (T - t1) time, where t1 < T; When the ambient temperature is lower than the second preset threshold, automatically shorten the pulse period T and extend the t1 time.

6. The method according to claim 1, wherein The method also includes an abnormal temperature handling mechanism: When the temperature of any partition exceeds the safety threshold, immediately cut off the heating power supply of this region and start the semiconductor refrigeration chip; Synchronously reduce the heating power of the adjacent regions until the temperature drops back to the target range; Record the environmental parameters and printing task characteristics when the abnormal event occurs, which are used to optimize the subsequent control strategy.

7. The method according to claim 1, wherein The method also includes an adaptive learning function: Collect the temperature deviation data, environmental parameter changes, and control response effects in the historical control process; Train through the reinforcement learning algorithm to generate the optimal PID parameter adjustment strategy; Update the training result to the fuzzy logic rule base to realize the self-iteration optimization of the control model.

8. An electronic device, characterized in that, It includes: A multi-sensor fusion module, which includes a distributed temperature sensor array, an ambient temperature and humidity sensor, and an ink flow monitoring unit; A data processing module for executing the control algorithm of the method according to any one of claims 1-7; A power driving module, including multiple independent PWM output ports, which are respectively connected to each partition heating unit and the heat dissipation device; A status monitoring module for real-time feedback of the working mode of the nozzle and temperature distribution data to the central controller.

9. The electronic device according to claim 8, wherein The hardware implementation of the power driving module includes: Using a multi-channel digital isolator to achieve electrical isolation between heating units; Each heating unit is equipped with an independent overcurrent protection circuit and a temperature fuse; The heat dissipation device includes a micro fan array and a thermoelectric cooler, and the cooling direction is controlled by an H-bridge circuit.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method according to claims 1-7 are implemented.

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